[GRASS-SVN] r49386 - grass/trunk/raster/r.texture
svn_grass at osgeo.org
svn_grass at osgeo.org
Sun Nov 27 09:06:22 EST 2011
Author: neteler
Date: 2011-11-27 06:06:22 -0800 (Sun, 27 Nov 2011)
New Revision: 49386
Modified:
grass/trunk/raster/r.texture/r.texture.html
Log:
restructured
Modified: grass/trunk/raster/r.texture/r.texture.html
===================================================================
--- grass/trunk/raster/r.texture/r.texture.html 2011-11-27 14:05:12 UTC (rev 49385)
+++ grass/trunk/raster/r.texture/r.texture.html 2011-11-27 14:06:22 UTC (rev 49386)
@@ -4,12 +4,18 @@
user-specified raster map layer. The module calculates textural features
based on spatial dependence matrices at 0, 45, 90, and 135
degrees for a <em>distance</em> (default = 1).
-<p><em>r.texture</em> assumes grey levels ranging from 0 to 255 as input.
-The input is rescaled to 0 to 255 if needed.
-<p>In general, several variables constitute texture: differences in grey level values,
+<p>
+<em>r.texture</em> assumes grey levels ranging from 0 to 255 as input.
+The input is automatically rescaled to 0 to 255 if the input map range is outside
+of this range.
+<p>
+In general, several variables constitute texture: differences in grey level values,
coarseness as scale of grey level differences, presence or lack of directionality
-and regular patterns.
-<p><em>r.texture</em> reads a GRASS raster map as input and calculates textural
+and regular patterns. A texture can be characterized by tone (grey level intensity
+properties) and structure (spatial relationships). Since textures are highly scale
+dependent, hierarchical textures may occur.
+<p>
+<em>r.texture</em> reads a GRASS raster map as input and calculates textural
features based on spatial
dependence matrices for north-south, east-west, northwest, and southwest
directions using a side by side neighborhood (i.e., a distance of 1). The user
@@ -18,14 +24,48 @@
The output consists into four images for each textural feature, one for every
direction.
-<p>A commonly used texture model is based on the so-called grey level co-occurrence
+<p>
+A commonly used texture model is based on the so-called grey level co-occurrence
matrix. This matrix is a two-dimensional histogram of grey levels
for a pair of pixels which are separated by a fixed spatial relationship.
The matrix approximates the joint probability distribution of a pair of pixels.
Several texture measures are directly computed from the grey level co-occurrence
matrix.
-<p>The following are brief explanations of texture measures:
-<p><ul>
+<p>
+The following part offers brief explanations of texture measures (after
+Jensen 1996).
+
+<h3>First-order statistics in the spatial domain</h3>
+<ul>
+<li> Sum Average (SA)</li>
+
+<li> Entropy (ENT):
+ This measure analyses the randomness. It is high when the values of the
+ moving window have similar values. It is low when the values are close
+ to either 0 or 1 (i.e. when the pixels in the local window are uniform).</li>
+
+<li> Difference Entropy (DE)</li>
+
+<li> Sum Entropy (SE)</li>
+
+<li> Variance (VAR):
+ A measure of gray tone variance within the moving window (second-order
+moment about the mean)</li>
+
+<li> Difference Variance (DV)</li>
+
+<li> Sum Variance (SV)</li>
+</ul>
+
+Note that measures "mean", "kurtosis", "range", "skewness", and "standard
+deviation" are available in <em>r.neighbors</em>.
+
+<h3>Second-order statistics in the spatial domain</h3>
+
+The second-order statistics texture model is based on the so-called grey
+level co-occurrence matrices (GLCM; after Haralick 1979).
+
+<ul>
<li> Angular Second Moment (ASM, also called Uniformity):
This is a measure of local homogeneity and the opposite of Entropy.
High values of ASM occur when the pixels in the moving window are
@@ -39,35 +79,19 @@
local homogeneity of a digital image. Low values are associated with low homogeneity
and vice versa.</li>
-<li> Contrast (Contr):
+<li> Contrast (CON):
This measure analyses the image contrast (locally gray-level variations) as
the linear dependency of grey levels of neighboring pixels (similarity). Typically high,
when the scale of local texture is larger than the <em>distance</em>.</li>
-<li> Correlation (Corr):
+<li> Correlation (COR):
This measure analyses the linear dependency of grey levels of neighboring
pixels. Typically high, when the scale of local texture is larger than the
<em>distance</em>.</li>
-<li> Variance (Var): A measure of gray tone variance within the moving
- window (second-order moment about the mean)</li>
+<li> Information Measures of Correlation (MOC)</li>
-<li> Difference Variance (DV): ...</li>
-
-<li> Sum Variance (SV): ... </li>
-
-<li> Sum Average (SA): ...</li>
-
-<li> Entropy (Entr):
- This measure analyses the randomness. It is high when the values of the moving
- window have similar values. It is low when the values are close to either 0 or 1 (i.e. when the
- pixels in the local window are uniform).</li>
-
-<li> Difference Entropy (DE): ...</li>
-
-<li> Sum Entropy (SE): ...</li>
-
-<li> Information Measures of Correlation (MOC): ...</li>
+<li> Maximal Correlation Coefficient (MCC)</li>
</ul>
<h2>NOTES</h2>
@@ -99,12 +123,13 @@
<h2>REFERENCES</h2>
-The algorithm was implemented after Haralick et al., 1973.
+The algorithm was implemented after Haralick et al., 1973 and 1979.
-<p>The code was taken by permission from <em>pgmtexture</em>, part of
+<p>
+The code was taken by permission from <em>pgmtexture</em>, part of
PBMPLUS (Copyright 1991, Jef Poskanser and Texas Agricultural Experiment
-Station, employer for hire of James Darrell McCauley). <br>
-Manual page of <a href="http://netpbm.sourceforge.net/doc/pgmtexture.html">pgmtexture</a>
+Station, employer for hire of James Darrell McCauley). Manual page
+of <a href="http://netpbm.sourceforge.net/doc/pgmtexture.html">pgmtexture</a>.
<ul>
<li>Haralick, R.M., K. Shanmugam, and I. Dinstein (1973). Textural features for
@@ -112,6 +137,8 @@
Cybernetics</em>, SMC-3(6):610-621.</li>
<li>Bouman, C. A., Shapiro, M. (1994). A Multiscale Random Field Model for
Bayesian Image Segmentation, IEEE Trans. on Image Processing, vol. 3, no. 2.</li>
+<li>Jensen, J.R. (1996). Introductory digital image processing. Prentice Hall.
+ ISBN 0-13-205840-5 </li>
<li>Haralick, R. (May 1979). <i>Statistical and structural approaches to texture</i>,
Proceedings of the IEEE, vol. 67, No.5, pp. 786-804</li>
<li>Hall-Beyer, M. (2007). <a href="http://www.fp.ucalgary.ca/mhallbey/tutorial.htm">The GLCM Tutorial Home Page</a>
@@ -124,6 +151,7 @@
<a href="i.smap.html">i.smap</a>,
<a href="i.gensigset.html">i.gensigset</a>,
<a href="i.pca.html">i.pca</a>,
+<a href="r.neighbors.html">r.neighbors</a>,
<a href="r.rescale.html">r.rescale</a>
</em>
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